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Food quality and safety issues have always been imperative topics discussed by people. The anti-tampering of blockchain technology and the transparency of information make it possible to improve food traceability and safety quality. How to effectively apply blockchain traceability technology to food safety has great research significance for improving food safety and consumer quality trust.

The paper aims to analyze the differences in product quality levels and market participants’ profits before and after the use of blockchain-driven traceability technology in the food agricultural product supply chain (SC) in the dynamic game frameworks of supplier-led and retailer-led modes, respectively, and explores the willingness, social welfare and consumer surplus of each member of the agricultural product SC to participate in the blockchain. Besides, We investigate the SC performance improvement with the mechanism of central centralized decision-making and revenue-sharing contract, compared to the SC performance in dynamic games.

The results are obtained as follow: The adoption of blockchain traceability technology can help improve the quality of food agricultural products, consumer surplus and social welfare, but the application and popularization of technology is hindered by traceability technology installment costs. Compared with the supplier leadership model, retailer-led food quality level, customer surplus and social welfare are higher.

How to effectively apply blockchain traceability technology to food safety has great research significance for improving food safety and consumer quality trust.

Food quality and safety issues have always been hot topics discussed by people. The anti-tampering of blockchain technology and the transparency of information make it possible to improve food traceability and safety quality.

The research results enrich the theories related to food safety and quality, and provide a valuable reference for food enterprises involved in the decision-making exploration of blockchain technology.

Based on the characteristics of blockchain technology, the demand function is adjusted and the product loss risk of channel members is transferred through a Stackelberg game SC composed of agricultural products suppliers and retailers.

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  We introduce two features of blockchain: quality trust and product information tracking.

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  The willingness of each member of the supply chain to use blockchain for product traceability was explored.

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  The overall traceability effect of the retailer-led blockchain is better than that of the manufacturer-led blockchain.

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  The cost of blockchain technology is a barrier to its adoption.

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  Blockchain brings higher consumer surplus and social welfare.

We introduce two features of blockchain: quality trust and product information tracking.

The willingness of each member of the supply chain to use blockchain for product traceability was explored.

The overall traceability effect of the retailer-led blockchain is better than that of the manufacturer-led blockchain.

The cost of blockchain technology is a barrier to its adoption.

Blockchain brings higher consumer surplus and social welfare.

In today’s age of globalization, every industry puts well-determined efforts toward surviving in the market. Industries are well aware of the fact that offering quality products and improving customer satisfaction is the strategic decision toward successful outcomes. During the recent years, food companies have undergone remarkable growth and development worldwide. In the Middle East, with a wide variety of demand and range of cultures, Iran is leading the food industries and possessed a top position of paramount importance. The present research aims to identify and prioritize barriers to implementing total quality management (TQM) in the Solico Food and Beverage Production Group (SFBPG) as a case study.

Firstly, an initial list of barriers is prepared based on a literature review. The identified barriers are then classified into four groups namely behavioral, technical-structural, human and cultural and strategic barriers based on the viewpoint of an expert team at a well-known food company. Secondly, the barriers are prioritized by adopting a special approach to multi-criteria decision-making (MCDM) called the Group Best-Worst Method (GBWM).

The obtained results reveal that the most substantial barriers prohibiting the successful implementation of TQM are lack of top management commitment and participation (0.334), high organizational burnout rate (0.128), instability and frequent changes of senior managers (0.123).

In general, this research has ranked the barriers from the most important one to the least significant instance. Additionally, this can enable managers and practitioners in the food industry to make worthy decisions and suggest multiple solutions to cope with obstacles to the successful implementation of TQM.

The purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.

This study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.

The findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.

Process mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.

Inspired by a food bank distribution operation, this paper aims to study synchronized vehicle routing for equitable and effective food allocation. The primary goal is to improve operational efficiency while ensuring equitable and effective food distribution among the partner agencies.

This study introduces a multiobjective Mixed Integer Programming (MIP) model aimed at addressing the complex challenge of effectively distributing food, particularly for food banks serving vulnerable populations in low-income urban and rural areas. The optimization approach described in this paper places a significant emphasis on social and economic considerations by fairly allocating food to food bank partner agencies while minimizing routing distance and waste. To assess the performance of the approach, this paper evaluates three distinct models, focusing on key performance measures such as effectiveness, equity and efficiency. The paper conducts a comprehensive numerical analysis using randomly generated data to gain insights into the trade-offs that arise and provide valuable managerial insights for food bank managers.

The results of the analysis highlight the models that perform better in terms of equity and effectiveness. Additionally, the results show that restocking the vehicles through the concept of synchronization improves the overall quantity of food allocation to partner agencies, thereby increasing accessibility.

This paper contributes significantly to the literature on optimization approaches in the field of humanitarian logistics.

This study provides food bank managers with three different models, each with a multifaceted nature of trade-offs, to better address the complex challenges of food insecurity.

This paper contributes significantly to social responsibility by enhancing the operational efficiency of food banks, ultimately improving their ability to serve communities in need.

To the best of the authors’ knowledge, this paper is the first to propose and analyze this new variant of vehicle routing problems in nonprofit settings.

Front-of-packaging (FOP) is a critical branding tool that uses “cues” to communicate product attributes and establish distinct brand images. This paper aims to understand how food brands utilize cues and their relative proportions to hierarchically communicate brand image and belonging to particular subcategories.

A content analysis is used for analysing 543 food FOPs sold in Australia (breakfast cereals, chips, snack bars). Samples are collected and classified into product sub-categories defined by ingredients, consumer-audience and retail placement. A novel 10 × 10 coding grid is applied to each FOP to objectively analyse cue proportion, with statistical comparison undertaken between sub-categories.

Results reveal intrinsic cues are favoured over extrinsic cues, except for those in the eatertainment sub-category. Hierarchies are evidenced that treat product and branding cues as primary, with health cues secondary. Statistically significant differences in cue proportions are consistently evident across breakfast cereals, chips and snack-bar FOPs. Clear differentiation is evidenced through cue proportions on FOP for health/nutrition focused sub-categories and eatertainment foods.

“Cue utilization theory” research is extended to an evaluation of brand encoding (not consumer decoding). Design conventions reveal how cue proportions establish a dialogue of communicating brand/product image hierarchically, the trade-offs that occur, a “meso-level” to Gestalt theory, and achieving categorization through FOP cue proportions. Deeper understanding of packaging design techniques provides inter-disciplinary insights that extend consumer behaviour, retailing and design scholarship.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

This study aims to explore the relationship between Generation Z (or Gen Z) consumers’ decision-making styles and electronic word-of-mouth (eWOM) use in the tourism sector. Drawing on the consumer style inventory (CSI) model and the theory of reasoned action (TRA), the research examines how specific decision-making styles influence Gen Z’s propensity to use eWOM recommendations for accommodation choices.

The study uses structural equation modelling to analyse data collected from 296 Gen Z users of Booking.com. The CSI model is adapted to the analysed context and attributes – impulsive, recreational, sustainable, fashion-conscious and perfectionist attitudes – are examined to determine their impact on eWOM use intention and actual eWOM use.

Three of the hypothesised relationships in the model were validated. Specifically, the results suggest that the attitudes of sustainable and perfectionist consumers influence the intention to use eWOM. Furthermore, use intention is positively associated with the actual use of eWOM.

For marketers and tourism businesses, understanding the decision-making styles of Gen Z can inform the development of targeted marketing strategies that emphasise quality and sustainability. Highlighting these aspects in online reviews and eWOM platforms can enhance engagement with Gen Z consumers.

This research advances the understanding of eWOM behaviour by integrating CSI and TRA theories in the context of Gen Z’s tourism decision-making. It provides empirical evidence on the significant role of perfectionist and sustainable attitudes in shaping eWOM intentions, contributing to the literature on consumer behaviour and digital marketing in tourism.

The rise of artificial intelligence (AI) and machine learning has largely promoted the emergence of “autonomous decision-making” (ADM). This paper aims to establish a personalized artificial intelligent housekeeper (AIH) that knows more about our hobbies, habits, personality traits, and shopping needs than ourselves and can replace us to do some habitual purchasing behavior.

We propose an AI decision-making method based on machine learning algorithm, a novel framework for personalized customer preference and purchase. First, the method uses interactive big data to predict a potential consumer’s decision possibility. Then, the method mines the correlation between consumer decision possibility and various factors affecting consumer behavior. Finally, the machine learning algorithm is used to estimate the consumer’s purchase decision according to the comprehensive influencing factors data of the target consumer.

The experimental results show that the method can predict the regular consumption behavior of consumers in advance and make accurate decision-making behavior. It can find correlations from a large amount of data to help predict many simple purchase decisions in our life, and become our AIH.

This study introduces a new approach that not only has the auxiliary decision-making function but also has the decision-making function. These findings contribute to the research on automated decision-making process of AI and on human–technology interaction by investigating how data attributes consumer purchase decision to AI.

Recent technological advancements, often linked to Industry 4.0, require organizations to be more agile and innovative. Blockchain technology (BT) holds immense potential in driving organizations to achieve efficiency and transparency in supply chains. However, there exist some insurmountable challenges associated with the adoption of BT in organizational supply chains (SC). This paper attempts to categorically identify and systematize the most influential challenges in the implementation of BT in SC.

This study resorts to an extensive literature review and consultations with experts in the field of supply chain management (SCM), information technology and academia to identify, categorize and prioritize the major challenges using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Combined Compromise Solution method (CoCoSo).

The top three classes of challenges revealed in this study are privacy challenges (PC), infrastructure challenges (IC) and transparency challenges (TC). Maintaining a balance between data openness and secrecy and rectification of incorrect/erroneous input are the top two challenges in the PC category, integration of BT with sustainable practices and ensuring legitimacy are the top two challenges in the IC category, and proper and correct information sharing in organizations was the top most challenge in the TC category.

Future scholars and industry professionals will be guided by the importance of the challenges identified in this study to develop an economical and logical approach for integrating BT to increase the efficiency and outcome of supply chains across several industrial sectors.